EDA Intelligent evolutionary design: A new approach to optimizing complex engineering systems and its application to designing heat exchangers

Learnable Evolution Model is essentially EDA (estimation of distribution algorithm)

Research Article

Intelligent evolutionary design: A new approach to optimizing complex engineering systems and its application to designing heat exchangers

Ryszard S. Michalski 1 2 *, Kenneth A. Kaufman 1 *

1Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030, USA 2Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

email: Ryszard S. Michalski (michalski@mli.gmu.edu) Kenneth A. Kaufman (ken.kaufman@gmail.com)

^*Correspondence to Ryszard S. Michalski, Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030, USA

^*Correspondence to Kenneth A. Kaufman, Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030, USA

Funded by:
National Science Foundation; Grant Number: IIS-0097476, IIS-9906858
UMBC/LUCITE; Grant Number: #32

Abstract

A new method for optimizing complex engineering designs is presented that is based on the Learnable Evolution Model (LEM), a recently developed form of non-Darwinian evolutionary computation. Unlike conventional Darwinian-type methods that execute an unguided evolutionary process, the proposed method, called LEMd, guides the evolutionary design process using a combination of two methods, one involving computational intelligence and the other involving encoded expert knowledge. Specifically, LEMd integrates two modes of operation, Learning Mode and Probing Mode. Learning Mode applies a machine learning program to create new designs through hypothesis generation and instantiation, whereas Probing Mode creates them by applying expert-suggested design modification operators tailored to the specific design problem. The LEMd method has been used to implement two initial systems, ISHED1 and ISCOD1, specialized for the optimization of evaporators and condensers in cooling systems, respectively. The designs produced by these systems matched or exceeded in performance the best designs developed by human experts. These promising results and the generality of the presented method suggest that LEMd offers a powerful new tool for optimizing complex engineering systems. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1217-1248, 2006.

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Digital Object Identifier (DOI)

10.1002/int.20182 About DOI